- 🌐 Pioneered the concept of Gen AI with BERT and Transformers in 2019.
- 🤖 Built both open-domain and closed-domain chatbots utilizing DuckDuckGo API, Wikipedia API, and Custom dataset.
- 📊 Organized a 70-year NLP survey up to 2020, breathing life into the concept of chatbots with Python and NgRok-based web app using Google Colab with DNN models.
- 🌐 Tested it with a wide range of chatbots like OK Google, Amazon Assistant, and others, as detailed in my full thesis.
- 🏆 Awarded first place for this groundbreaking project.
- The proposed system aims to provide precise and selective answers from the web, eliminating the need for users to filter or parse through extensive search results.
- The system is capable of handling open-domain questions, making it predict answers almost every time. Privacy is a significant focus in open-domain search, utilizing DuckDuckGo and Wikipedia APIs for search.
- It is easily implementable and customizable for specific tasks within a particular application domain. The system is cost-effective and well-suited for use in any browser. Additionally, the system can provide chit-chat answers, reducing traditional knowledge gathering/learning time.
- Employing Deep Learning (DL)-based NLP technology helps minimize bot misunderstandings, resulting in more specific answers compared to traditional systems.
Notice: This project was conceived before the Gen AI era. I have made the code and documentation completely available under the GPL license. Some of the DL and NLP modules may have been updated in the past 3-4 years, and as a result, the AI Chatbot might not work. However, this project marked the beginning of conceptualizing BERT-like transformers for Gen AI tasks. I am proud to have pioneered this concept and was awarded first place for my B.E. thesis.